Modern enterprises are large complex systems operating in highly dynamic environments thus requiring to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We outline a knowledge-guided simulation-Aided data-driven model-based evidence-backed approach to make enterprises adaptive. The approach hinges on the concept of Digital Twin-a set of relevant models that are amenable to analysis and simulation. We describe the core modeling and model processing infrastructure developed, and early stage explorations of its application to problems where the mechanistic world view holds. We argue similar benefits are possible for problem spaces involving human actors as well.